Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations187
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.2 KiB
Average record size in memory236.5 B

Variable types

Text1
Numeric13
Categorical1

Alerts

Deaths / 100 Recovered has 5 (2.7%) infinite valuesInfinite
Country/Region has unique valuesUnique
Deaths has 17 (9.1%) zerosZeros
Recovered has 6 (3.2%) zerosZeros
Active has 5 (2.7%) zerosZeros
New cases has 33 (17.6%) zerosZeros
New deaths has 91 (48.7%) zerosZeros
New recovered has 61 (32.6%) zerosZeros
Deaths / 100 Cases has 17 (9.1%) zerosZeros
Recovered / 100 Cases has 6 (3.2%) zerosZeros
Deaths / 100 Recovered has 17 (9.1%) zerosZeros
1 week change has 12 (6.4%) zerosZeros
1 week % increase has 12 (6.4%) zerosZeros

Reproduction

Analysis started2025-11-30 19:07:01.102718
Analysis finished2025-11-30 19:07:21.410892
Duration20.31 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Country/Region
Text

Unique 

Distinct187
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
2025-11-30T20:07:21.651841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length22
Mean length8.4331551
Min length2

Characters and Unicode

Total characters1577
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola
ValueCountFrequency (%)
and7
 
3.0%
saint3
 
1.3%
south3
 
1.3%
guinea3
 
1.3%
sudan2
 
0.9%
republic2
 
0.9%
united2
 
0.9%
congo2
 
0.9%
new2
 
0.9%
brazil1
 
0.4%
Other values (207)207
88.5%
2025-11-30T20:07:22.093532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a247
15.7%
i140
 
8.9%
n128
 
8.1%
e111
 
7.0%
r88
 
5.6%
o84
 
5.3%
t58
 
3.7%
u56
 
3.6%
l51
 
3.2%
d49
 
3.1%
Other values (47)565
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a247
15.7%
i140
 
8.9%
n128
 
8.1%
e111
 
7.0%
r88
 
5.6%
o84
 
5.3%
t58
 
3.7%
u56
 
3.6%
l51
 
3.2%
d49
 
3.1%
Other values (47)565
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a247
15.7%
i140
 
8.9%
n128
 
8.1%
e111
 
7.0%
r88
 
5.6%
o84
 
5.3%
t58
 
3.7%
u56
 
3.6%
l51
 
3.2%
d49
 
3.1%
Other values (47)565
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a247
15.7%
i140
 
8.9%
n128
 
8.1%
e111
 
7.0%
r88
 
5.6%
o84
 
5.3%
t58
 
3.7%
u56
 
3.6%
l51
 
3.2%
d49
 
3.1%
Other values (47)565
35.8%

Confirmed
Real number (ℝ)

Distinct184
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88130.936
Minimum10
Maximum4290259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:22.380406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile33.3
Q11114
median5059
Q340460.5
95-th percentile287810.9
Maximum4290259
Range4290249
Interquartile range (IQR)39346.5

Descriptive statistics

Standard deviation383318.66
Coefficient of variation (CV)4.3494224
Kurtosis86.096572
Mean88130.936
Median Absolute Deviation (MAD)4973
Skewness8.7256762
Sum16480485
Variance1.469332 × 1011
MonotonicityNot monotonic
2025-11-30T20:07:22.562875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242
 
1.1%
862
 
1.1%
106212
 
1.1%
1095971
 
0.5%
91321
 
0.5%
18431
 
0.5%
187521
 
0.5%
534131
 
0.5%
15571
 
0.5%
34391
 
0.5%
Other values (174)174
93.0%
ValueCountFrequency (%)
101
0.5%
121
0.5%
141
0.5%
171
0.5%
181
0.5%
201
0.5%
231
0.5%
242
1.1%
271
0.5%
481
0.5%
ValueCountFrequency (%)
42902591
0.5%
24423751
0.5%
14800731
0.5%
8166801
0.5%
4525291
0.5%
3954891
0.5%
3897171
0.5%
3479231
0.5%
3017081
0.5%
2936061
0.5%

Deaths
Real number (ℝ)

Zeros 

Distinct150
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3497.5187
Minimum0
Maximum148011
Zeros17
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:22.724634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.5
median108
Q3734
95-th percentile15138.6
Maximum148011
Range148011
Interquartile range (IQR)715.5

Descriptive statistics

Standard deviation14100.002
Coefficient of variation (CV)4.0314302
Kurtosis66.480494
Mean3497.5187
Median Absolute Deviation (MAD)106
Skewness7.4644811
Sum654036
Variance1.9881007 × 108
MonotonicityNot monotonic
2025-11-30T20:07:22.905957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017
 
9.1%
114
 
2.1%
13
 
1.6%
103
 
1.6%
23
 
1.6%
83
 
1.6%
73
 
1.6%
452
 
1.1%
352
 
1.1%
222
 
1.1%
Other values (140)145
77.5%
ValueCountFrequency (%)
017
9.1%
13
 
1.6%
23
 
1.6%
32
 
1.1%
41
 
0.5%
51
 
0.5%
61
 
0.5%
73
 
1.6%
83
 
1.6%
91
 
0.5%
ValueCountFrequency (%)
1480111
0.5%
876181
0.5%
458441
0.5%
440221
0.5%
351121
0.5%
334081
0.5%
302121
0.5%
284321
0.5%
184181
0.5%
159121
0.5%

Recovered
Real number (ℝ)

Zeros 

Distinct178
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50631.481
Minimum0
Maximum1846641
Zeros6
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:23.074198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.6
Q1626.5
median2815
Q322606
95-th percentile235599
Maximum1846641
Range1846641
Interquartile range (IQR)21979.5

Descriptive statistics

Standard deviation190188.19
Coefficient of variation (CV)3.7563228
Kurtosis55.600771
Mean50631.481
Median Absolute Deviation (MAD)2789
Skewness6.9836438
Sum9468087
Variance3.6171547 × 1010
MonotonicityNot monotonic
2025-11-30T20:07:23.253123image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
3.2%
182
 
1.1%
8032
 
1.1%
1282
 
1.1%
392
 
1.1%
251981
 
0.5%
87521
 
0.5%
137541
 
0.5%
1891
 
0.5%
15141
 
0.5%
Other values (168)168
89.8%
ValueCountFrequency (%)
06
3.2%
81
 
0.5%
111
 
0.5%
121
 
0.5%
131
 
0.5%
151
 
0.5%
182
 
1.1%
191
 
0.5%
221
 
0.5%
231
 
0.5%
ValueCountFrequency (%)
18466411
0.5%
13258041
0.5%
9511661
0.5%
6022491
0.5%
3199541
0.5%
3038101
0.5%
2749251
0.5%
2725471
0.5%
2551441
0.5%
2410261
0.5%

Active
Real number (ℝ)

Zeros 

Distinct173
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34001.936
Minimum0
Maximum2816444
Zeros5
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:23.404050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1141.5
median1600
Q39149
95-th percentile98399.5
Maximum2816444
Range2816444
Interquartile range (IQR)9007.5

Descriptive statistics

Standard deviation213326.17
Coefficient of variation (CV)6.273942
Kurtosis157.92166
Mean34001.936
Median Absolute Deviation (MAD)1585
Skewness12.182067
Sum6358362
Variance4.5508056 × 1010
MonotonicityNot monotonic
2025-11-30T20:07:23.561962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
2.7%
13
 
1.6%
23
 
1.6%
15992
 
1.1%
212
 
1.1%
132
 
1.1%
92
 
1.1%
242
 
1.1%
522
 
1.1%
19201
 
0.5%
Other values (163)163
87.2%
ValueCountFrequency (%)
05
2.7%
13
1.6%
23
1.6%
41
 
0.5%
81
 
0.5%
92
 
1.1%
121
 
0.5%
132
 
1.1%
151
 
0.5%
181
 
0.5%
ValueCountFrequency (%)
28164441
0.5%
5081161
0.5%
4954991
0.5%
2544271
0.5%
2010971
0.5%
1705371
0.5%
1171631
0.5%
1089281
0.5%
1075141
0.5%
987521
0.5%

New cases
Real number (ℝ)

Zeros 

Distinct122
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1222.9572
Minimum0
Maximum56336
Zeros33
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:23.713696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median49
Q3419.5
95-th percentile2706.3
Maximum56336
Range56336
Interquartile range (IQR)415.5

Descriptive statistics

Standard deviation5710.3748
Coefficient of variation (CV)4.6693169
Kurtosis65.02233
Mean1222.9572
Median Absolute Deviation (MAD)49
Skewness7.7203196
Sum228693
Variance32608380
MonotonicityNot monotonic
2025-11-30T20:07:23.858739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
17.6%
16
 
3.2%
114
 
2.1%
243
 
1.6%
103
 
1.6%
43
 
1.6%
33
 
1.6%
23
 
1.6%
133
 
1.6%
53
 
1.6%
Other values (112)123
65.8%
ValueCountFrequency (%)
033
17.6%
16
 
3.2%
23
 
1.6%
33
 
1.6%
43
 
1.6%
53
 
1.6%
63
 
1.6%
73
 
1.6%
81
 
0.5%
91
 
0.5%
ValueCountFrequency (%)
563361
0.5%
444571
0.5%
232841
0.5%
163061
0.5%
137561
0.5%
70961
0.5%
56071
0.5%
49731
0.5%
48901
0.5%
27721
0.5%

New deaths
Real number (ℝ)

Zeros 

Distinct38
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.957219
Minimum0
Maximum1076
Zeros91
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:24.013708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile92.7
Maximum1076
Range1076
Interquartile range (IQR)6

Descriptive statistics

Standard deviation120.03717
Coefficient of variation (CV)4.145328
Kurtosis40.101549
Mean28.957219
Median Absolute Deviation (MAD)1
Skewness5.9700334
Sum5415
Variance14408.923
MonotonicityNot monotonic
2025-11-30T20:07:24.159839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
091
48.7%
117
 
9.1%
211
 
5.9%
37
 
3.7%
67
 
3.7%
47
 
3.7%
56
 
3.2%
173
 
1.6%
113
 
1.6%
202
 
1.1%
Other values (28)33
 
17.6%
ValueCountFrequency (%)
091
48.7%
117
 
9.1%
211
 
5.9%
37
 
3.7%
47
 
3.7%
56
 
3.2%
67
 
3.7%
72
 
1.1%
82
 
1.1%
92
 
1.1%
ValueCountFrequency (%)
10761
0.5%
6371
0.5%
6141
0.5%
5751
0.5%
5081
0.5%
3421
0.5%
2981
0.5%
2121
0.5%
1201
0.5%
961
0.5%

New recovered
Real number (ℝ)

Zeros 

Distinct103
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean933.81283
Minimum0
Maximum33728
Zeros61
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:24.310961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22
Q3221
95-th percentile2446.2
Maximum33728
Range33728
Interquartile range (IQR)221

Descriptive statistics

Standard deviation4197.7196
Coefficient of variation (CV)4.4952473
Kurtosis47.910082
Mean933.81283
Median Absolute Deviation (MAD)22
Skewness6.7695674
Sum174623
Variance17620850
MonotonicityNot monotonic
2025-11-30T20:07:24.485451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061
32.6%
26
 
3.2%
45
 
2.7%
14
 
2.1%
63
 
1.6%
242
 
1.1%
392
 
1.1%
702
 
1.1%
152
 
1.1%
1032
 
1.1%
Other values (93)98
52.4%
ValueCountFrequency (%)
061
32.6%
14
 
2.1%
26
 
3.2%
32
 
1.1%
45
 
2.7%
52
 
1.1%
63
 
1.6%
71
 
0.5%
81
 
0.5%
112
 
1.1%
ValueCountFrequency (%)
337281
0.5%
335981
0.5%
279411
0.5%
114941
0.5%
98481
0.5%
85881
0.5%
46971
0.5%
35921
0.5%
30771
0.5%
26131
0.5%

Deaths / 100 Cases
Real number (ℝ)

Zeros 

Distinct145
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0195187
Minimum0
Maximum28.56
Zeros17
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:24.647494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.945
median2.15
Q33.875
95-th percentile9.439
Maximum28.56
Range28.56
Interquartile range (IQR)2.93

Descriptive statistics

Standard deviation3.4543025
Coefficient of variation (CV)1.1439911
Kurtosis17.541183
Mean3.0195187
Median Absolute Deviation (MAD)1.35
Skewness3.3521727
Sum564.65
Variance11.932206
MonotonicityNot monotonic
2025-11-30T20:07:24.932037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017
 
9.1%
1.283
 
1.6%
2.913
 
1.6%
3.452
 
1.1%
0.952
 
1.1%
0.262
 
1.1%
1.332
 
1.1%
4.822
 
1.1%
1.412
 
1.1%
1.692
 
1.1%
Other values (135)150
80.2%
ValueCountFrequency (%)
017
9.1%
0.051
 
0.5%
0.151
 
0.5%
0.181
 
0.5%
0.262
 
1.1%
0.272
 
1.1%
0.361
 
0.5%
0.391
 
0.5%
0.431
 
0.5%
0.451
 
0.5%
ValueCountFrequency (%)
28.561
0.5%
15.191
0.5%
14.791
0.5%
14.261
0.5%
13.711
0.5%
13.41
0.5%
11.531
0.5%
11.131
0.5%
10.441
0.5%
101
0.5%

Recovered / 100 Cases
Real number (ℝ)

Zeros 

Distinct177
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.820535
Minimum0
Maximum100
Zeros6
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:25.091457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.775
Q148.77
median71.32
Q386.885
95-th percentile96.309
Maximum100
Range100
Interquartile range (IQR)38.115

Descriptive statistics

Standard deviation26.287694
Coefficient of variation (CV)0.40554578
Kurtosis-0.11572822
Mean64.820535
Median Absolute Deviation (MAD)17.31
Skewness-0.82336589
Sum12121.44
Variance691.04287
MonotonicityNot monotonic
2025-11-30T20:07:25.248508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
3.2%
1003
 
1.6%
75.612
 
1.1%
80.642
 
1.1%
76.822
 
1.1%
74.011
 
0.5%
97.241
 
0.5%
72.461
 
0.5%
90.721
 
0.5%
44.21
 
0.5%
Other values (167)167
89.3%
ValueCountFrequency (%)
06
3.2%
0.351
 
0.5%
0.481
 
0.5%
5.481
 
0.5%
8.531
 
0.5%
12.681
 
0.5%
17.741
 
0.5%
20.041
 
0.5%
20.251
 
0.5%
20.411
 
0.5%
ValueCountFrequency (%)
1003
1.6%
98.381
 
0.5%
98.331
 
0.5%
97.871
 
0.5%
97.241
 
0.5%
97.021
 
0.5%
96.61
 
0.5%
96.511
 
0.5%
95.841
 
0.5%
95.241
 
0.5%

Deaths / 100 Recovered
Real number (ℝ)

Infinite  Zeros 

Distinct155
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite5
Infinite (%)2.7%
Meaninf
Minimum0
Maximuminf
Zeros17
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:25.432072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.45
median3.62
Q36.44
95-th percentile32.982
Maximuminf
Rangeinf
Interquartile range (IQR)4.99

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)2.45
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2025-11-30T20:07:25.592390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017
 
9.1%
inf5
 
2.7%
4.862
 
1.1%
3.912
 
1.1%
12.122
 
1.1%
6.692
 
1.1%
1.452
 
1.1%
3.012
 
1.1%
1.912
 
1.1%
5.252
 
1.1%
Other values (145)149
79.7%
ValueCountFrequency (%)
017
9.1%
0.061
 
0.5%
0.161
 
0.5%
0.21
 
0.5%
0.331
 
0.5%
0.351
 
0.5%
0.391
 
0.5%
0.511
 
0.5%
0.521
 
0.5%
0.551
 
0.5%
ValueCountFrequency (%)
inf5
2.7%
3259.261
 
0.5%
3190.261
 
0.5%
57.981
 
0.5%
56.281
 
0.5%
37.21
 
0.5%
23.141
 
0.5%
18.911
 
0.5%
17.91
 
0.5%
17.681
 
0.5%

Confirmed last week
Real number (ℝ)

Distinct183
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78682.476
Minimum10
Maximum3834677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-11-30T20:07:25.771216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile24.9
Q11051.5
median5020
Q337080.5
95-th percentile273170.2
Maximum3834677
Range3834667
Interquartile range (IQR)36029

Descriptive statistics

Standard deviation338273.68
Coefficient of variation (CV)4.2992251
Kurtosis89.376884
Mean78682.476
Median Absolute Deviation (MAD)4912
Skewness8.8651982
Sum14713623
Variance1.1442908 × 1011
MonotonicityNot monotonic
2025-11-30T20:07:25.949182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192
 
1.1%
5222
 
1.1%
19802
 
1.1%
232
 
1.1%
92491
 
0.5%
15071
 
0.5%
13441
 
0.5%
178441
 
0.5%
521321
 
0.5%
15551
 
0.5%
Other values (173)173
92.5%
ValueCountFrequency (%)
101
0.5%
121
0.5%
131
0.5%
171
0.5%
181
0.5%
192
1.1%
232
1.1%
241
0.5%
271
0.5%
401
0.5%
ValueCountFrequency (%)
38346771
0.5%
21186461
0.5%
11553381
0.5%
7762121
0.5%
3736281
0.5%
3576811
0.5%
3493961
0.5%
3330291
0.5%
2969441
0.5%
2762021
0.5%

1 week change
Real number (ℝ)

Zeros 

Distinct162
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9448.4599
Minimum-47
Maximum455582
Zeros12
Zeros (%)6.4%
Negative1
Negative (%)0.5%
Memory size1.6 KiB
2025-11-30T20:07:26.135817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-47
5-th percentile0
Q149
median432
Q33172
95-th percentile18508
Maximum455582
Range455629
Interquartile range (IQR)3123

Descriptive statistics

Standard deviation47491.128
Coefficient of variation (CV)5.0263353
Kurtosis61.662738
Mean9448.4599
Median Absolute Deviation (MAD)431
Skewness7.692012
Sum1766862
Variance2.2554072 × 109
MonotonicityNot monotonic
2025-11-30T20:07:26.292225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012
 
6.4%
14
 
2.1%
23
 
1.6%
723
 
1.6%
472
 
1.1%
2012
 
1.1%
272
 
1.1%
982
 
1.1%
92
 
1.1%
112
 
1.1%
Other values (152)153
81.8%
ValueCountFrequency (%)
-471
 
0.5%
012
6.4%
14
 
2.1%
23
 
1.6%
41
 
0.5%
51
 
0.5%
61
 
0.5%
71
 
0.5%
81
 
0.5%
92
 
1.1%
ValueCountFrequency (%)
4555821
0.5%
3247351
0.5%
3237291
0.5%
789011
0.5%
530961
0.5%
460931
0.5%
404681
0.5%
366421
0.5%
320361
0.5%
187721
0.5%

1 week % increase
Real number (ℝ)

Zeros 

Distinct169
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.606203
Minimum-3.84
Maximum226.32
Zeros12
Zeros (%)6.4%
Negative1
Negative (%)0.5%
Memory size1.6 KiB
2025-11-30T20:07:26.458324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-3.84
5-th percentile0
Q12.775
median6.89
Q316.855
95-th percentile37.277
Maximum226.32
Range230.16
Interquartile range (IQR)14.08

Descriptive statistics

Standard deviation24.509838
Coefficient of variation (CV)1.8013723
Kurtosis45.808865
Mean13.606203
Median Absolute Deviation (MAD)5.35
Skewness6.1146129
Sum2544.36
Variance600.73215
MonotonicityNot monotonic
2025-11-30T20:07:26.617141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012
 
6.4%
5.362
 
1.1%
6.892
 
1.1%
12.242
 
1.1%
2.512
 
1.1%
10.422
 
1.1%
3.132
 
1.1%
2.442
 
1.1%
9.281
 
0.5%
12.871
 
0.5%
Other values (159)159
85.0%
ValueCountFrequency (%)
-3.841
 
0.5%
012
6.4%
0.131
 
0.5%
0.261
 
0.5%
0.291
 
0.5%
0.491
 
0.5%
0.641
 
0.5%
0.681
 
0.5%
0.71
 
0.5%
0.781
 
0.5%
ValueCountFrequency (%)
226.321
0.5%
191.071
0.5%
119.541
0.5%
57.851
0.5%
42.781
0.5%
42.521
0.5%
41.571
0.5%
40.671
0.5%
37.441
0.5%
37.341
0.5%

WHO Region
Categorical

Distinct6
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
Europe
56 
Africa
48 
Americas
35 
Eastern Mediterranean
22 
Western Pacific
16 

Length

Max length21
Median length6
Mean length9.3903743
Min length6

Characters and Unicode

Total characters1756
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEastern Mediterranean
2nd rowEurope
3rd rowAfrica
4th rowEurope
5th rowAfrica

Common Values

ValueCountFrequency (%)
Europe56
29.9%
Africa48
25.7%
Americas35
18.7%
Eastern Mediterranean22
 
11.8%
Western Pacific16
 
8.6%
South-East Asia10
 
5.3%

Length

2025-11-30T20:07:26.789041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T20:07:26.955060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
europe56
23.8%
africa48
20.4%
americas35
14.9%
eastern22
 
9.4%
mediterranean22
 
9.4%
western16
 
6.8%
pacific16
 
6.8%
south-east10
 
4.3%
asia10
 
4.3%

Most occurring characters

ValueCountFrequency (%)
r221
12.6%
e211
12.0%
a185
10.5%
i147
 
8.4%
c115
 
6.5%
A93
 
5.3%
s93
 
5.3%
E88
 
5.0%
n82
 
4.7%
t80
 
4.6%
Other values (13)441
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1756
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r221
12.6%
e211
12.0%
a185
10.5%
i147
 
8.4%
c115
 
6.5%
A93
 
5.3%
s93
 
5.3%
E88
 
5.0%
n82
 
4.7%
t80
 
4.6%
Other values (13)441
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1756
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r221
12.6%
e211
12.0%
a185
10.5%
i147
 
8.4%
c115
 
6.5%
A93
 
5.3%
s93
 
5.3%
E88
 
5.0%
n82
 
4.7%
t80
 
4.6%
Other values (13)441
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1756
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r221
12.6%
e211
12.0%
a185
10.5%
i147
 
8.4%
c115
 
6.5%
A93
 
5.3%
s93
 
5.3%
E88
 
5.0%
n82
 
4.7%
t80
 
4.6%
Other values (13)441
25.1%

Interactions

2025-11-30T20:07:19.539387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:01.445627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:02.950012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:04.641306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:06.186430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:07.689470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:09.297933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:10.735605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:12.294235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:13.910918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.972060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:16.390066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:18.075352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:19.644326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:01.642311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:03.065809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:04.756328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:06.289249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:07.795234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:09.411099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:10.842987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:12.393692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.015591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:15.049085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:16.494336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:18.191732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:19.756668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:01.749909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:03.196137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:04.887874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:06.419010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:07.909681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:09.536878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:10.956529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:12.515945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.139513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:15.119803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:16.625163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:18.301682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:19.868089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:01.861584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:03.323468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:05.002160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:06.542016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:08.056507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:09.652991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:11.086852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:12.643450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.230064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:15.219484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:16.755839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:18.417690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:19.976037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:01.966561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:03.438643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:05.121086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:06.655962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:08.160266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:09.757821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:11.206068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:12.765495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.306588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:15.340704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:16.882016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:18.533764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:20.076557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:02.074875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:03.555630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:05.230036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:06.758035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:08.265391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:09.867619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:11.324510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:12.872910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.379541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:15.451583image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:16.996912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:18.653897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:20.201789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:02.179955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:03.672868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:05.345031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:06.872283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:08.376225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:09.962750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:11.443458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:12.982856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.454404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:15.557294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:17.122412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:18.761817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:20.341432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:02.308475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:03.798395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:05.469802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:07.000589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:08.489753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:10.071362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:11.563562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:13.110900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.534880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:15.686517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:17.250501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:18.870882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:20.447212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:02.404897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:03.907146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:05.590460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:07.123325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:08.712959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:10.178049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:11.683672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:13.350166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.608140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:15.794502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:17.478982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:18.979388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:20.560558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:02.508504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:04.016969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:05.695092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:07.239958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:08.844093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:10.275743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:11.800516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:13.460496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.675202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:15.915015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:17.598580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:19.086995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:20.664366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:02.616483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:04.285317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:05.820604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:07.348786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:08.977920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:10.384175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:11.929513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:13.576470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.753050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:16.024720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:17.714420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:19.200554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:20.783007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:02.756955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:04.420804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:05.959128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:07.472082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:09.101446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:10.510148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:12.056839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:13.703915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.829115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:16.159401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:17.853328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:19.334400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:20.882757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:02.854094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:04.533393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:06.075657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:07.585567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:09.204230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:10.623143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:12.171691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:13.799929image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:14.904029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:16.268588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:17.965271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:07:19.443212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Missing values

2025-11-30T20:07:21.029100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-30T20:07:21.295427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Country/RegionConfirmedDeathsRecoveredActiveNew casesNew deathsNew recoveredDeaths / 100 CasesRecovered / 100 CasesDeaths / 100 RecoveredConfirmed last week1 week change1 week % increaseWHO Region
0Afghanistan36263126925198979610610183.5069.495.04355267372.07Eastern Mediterranean
1Albania4880144274519911176632.9556.255.25417170917.00Europe
2Algeria27973116318837797361687494.1667.346.1723691428218.07Africa
3Andorra907528035210005.7388.536.48884232.60Europe
4Angola9504124266718104.3225.4716.9474920126.84Africa
5Antigua and Barbuda86365184053.4975.584.62761013.16Americas
6Argentina16741630597257591782489012020571.8343.354.211307743664228.02Americas
7Armenia3739071126665100147361871.9071.322.673498124096.89Europe
8Australia153031679311582536861371.0960.841.7912428287523.13Western Pacific
9Austria20558713182461599861373.4788.753.91197438154.13Europe
Country/RegionConfirmedDeathsRecoveredActiveNew casesNew deathsNew recoveredDeaths / 100 CasesRecovered / 100 CasesDeaths / 100 RecoveredConfirmed last week1 week change1 week % increaseWHO Region
177United Kingdom3017084584414372544276887315.190.483190.2629694447641.60Europe
178Uruguay12023595121610132.9179.123.68106413812.97Americas
179Uzbekistan2120912111674941467855690.5755.041.0417149406023.67Europe
180Venezuela159881469959588352542130.9162.291.4712334365429.63Americas
181Vietnam43103656611000.0084.690.003844712.24Western Pacific
182West Bank and Gaza106217837526791152200.7335.332.088916170519.12Eastern Mediterranean
183Western Sahara1018100010.0080.0012.501000.00Africa
184Yemen16914838333751043628.5649.2657.981619724.45Eastern Mediterranean
185Zambia4552140281515977114653.0861.844.973326122636.86Africa
186Zimbabwe27043654221261922241.3320.046.64171399157.85Africa